Adaptive color transformation to aid computer vision

ABSTRACT

System and techniques for adaptive color transformation to aid computer vision are described herein. Colors from an image are mapped into a multi-dimensional space to create a distribution of colors in the image. A line can be fit to the distribution. Here, the line includes an angle relative to a coordinate system of the multi-dimensional space. A transformation to colors can then be applied to the image based on the angle of the line. The transformation producing a reduced image where a color complexity of the original image is reduced.

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/509,158 filed Jul. 11, 2019, which claims the priority of U.S.Application No. 62/696,727 filed Jul. 11, 2008, which applications areincorporated in their entirety herein by reference.

TECHNICAL FIELD

Embodiments described herein generally relate to computer vision systemsfor vehicles and more specifically to adaptive color transformation inan image to aid computer vision.

BACKGROUND

Many crops that are farmed are row crops. Row crops are arranged intorows that are generally equally spaced parallel rows in a fieldseparated by furrows. Tending row crops generally involves passingagricultural equipment (AEQ) (e.g., tractors, planters, harvesters,irrigators, fertilizers, etc.) over the field. Generally, the AEQ shouldfollow the rows such that support structures (e.g., wheels, treads,skids, etc.) remain in the furrows so as not to damage the crops.Further, equipment dealing directly with the crops should follow thecenterline of the crop rows.

Navigation systems using an external location mechanism have beenemployed to facilitate automatic navigation of AEQ. These systemsinclude using global position system (GPS) units to locate the positionof AEQ with respect to crop rows. Generally, these systems use aninitialization operation to determine positions through which the AEQshould pass and then provide information about the current position ofAEQ in a field to facilitate navigation. An example initializationoperation can include using a GPS unit to record the movement of AEQ asthe row crops are planted. This recording can later be used to guide theAEQ for subsequent operations.

Computer vision (CV) can be used to guide AEQ down the crop rows. CV canbe superior to external location mechanisms when for example, theexternal location mechanism is compromised (e.g., has inaccurate orabsent positioning) or has not been initialized. A CV navigation systemgenerally involves a sensor, such as a camera, mounted on the AEQ tocollect features of the environment. These features can be used toascertain AEQ position relative to a crop related row (e.g., a crop rowor a furrow) positions and provide that information as parameters to asteering controller to control the AEQ.

Often, CV steering systems ascertain two guidance parameters that areprovided to the steering controller: track-angle error (TKE) andcross-track distance (XTK). TKE involves the angle between the forwarddirection of the AEQ and the rows such that, when the AEQ is followingthe rows the TKE is 0° and when the AEQ is moving parallel to the rowsthe TKE is 90°. Accordingly, the TKE can be considered the currentangle-of-attack for AEQ moving towards a given row. The XTK distance isthe distance between the current position of the AEQ and the croprelated row. Using TKE and XTK as parameters to the steering module canallow for an effective row guidance system of AEQ using CV. Thus, costsand error can be reduced in performing an initialization operation forGPS based systems, or for automatic navigation of AEQ when GPS, or othernavigation systems, are unavailable.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralscan describe similar components in different views. Like numerals havingdifferent letter suffixes can represent different instances of similarcomponents. The drawings illustrate generally, by way of example, butnot by way of limitation, various embodiments discussed in the presentdocument.

FIG. 1 is an example of an environment including a system for adaptivecolor transformation to aid CV, according to an embodiment.

FIGS. 2A-2B illustrate various components and relationships of an AEQ inan operating environment, according to an embodiment.

FIG. 3 illustrates mapping colors to a multi-dimensional space,according to an embodiment.

FIG. 4 illustrates fitting a line to a color distribution using linearregression, according to an embodiment.

FIG. 5 illustrates fitting a line to a color distribution usingclustering, according to an embodiment.

FIG. 6 illustrates fitting a line to a color distribution using anangular scan, according to an embodiment.

FIG. 7 illustrates fitting a line to a color distribution using a thetadistribution, according to an embodiment.

FIG. 8 illustrates a flow diagram of an example of a method for adaptivecolor transformation to aid CV, according to an embodiment.

FIG. 9 is a block diagram illustrating an example of a machine uponwhich one or more embodiments can be implemented.

DETAILED DESCRIPTION

Finding crop related rows is an important aspect in determining TKE andXTK in CV systems. An aspect of finding crop related rows in CV (e.g.,without the aid of range finders, depth cameras, etc.) is determiningwhich pixels correspond to a crop (e.g., a crop row) and which do not(e.g., a furrow). Generally, color information provides thisdistinction. However, the shades of green to red to yellow of crops andtan to brown to red of soil can, at times, blend together, makingdistinctions between crop rows and furrows difficult to detect. Further,processing full color images can involve significant resourceexpenditures to search for lines in the generally large color space.

To address these issues, an adaptive color transformation is describedherein. The color transformation reduces color complexity whileadaptively increasing contrast between the probably two main colorsrespectively corresponding to crop rows and furrows. In an example, thecolor complexity is reduced to a single value for each pixel, hereincalled an intensity image or intensity map. When rendered graphically,the intensity image can appear as a grayscale image. Reducing the colorcomplexity can reduce processing and storage used in line detection,resulting in lower latencies (e.g., real-time performance) and reducedequipment and power costs.

To increase the efficacy of the color transformation, the colorcomplexity reduction is performed by increasing contrast between the twodominant colors of the image, here assumed to a first color of the cropsand a second color of the soil or other substrate. To accomplish this, alinear relationship between color positions in the multidimensionalspace is established. One or more lines (e.g., vectors) representingthis linear relationship have one or more angles relative to thecoordinate system of the multi-dimensional space. These angles are usedin the transformation, translating the points to a constant in onedimension, the one being eliminated, via the angle. For example, givenan angle α for the line V, and a coordinate of color DP in twodimensions (e.g., represented by (x, y)), then the one-dimensionalrepresentation of DP can be z=DP×(cos α, sin α). In an example, theresult z can be scaled to fit preferred ranges. This can increaseefficiency, for example, by scaling z to fit in fixed size buffers,among other things. Additional details and examples are described below.

Systems that have access to disparity (e.g., depth) information, such asgenerated by stereo vision cameras or depth cameras, can use disparityinformation and CV to determine which pixels of an image of crop relatedrows correspond to a crop or a furrow. In an example a disparity map canbe generated such that pixels in the disparity map represent distances,or another equivalent unit such as depth, to camera. The disparity mapcan serve as a basis for finding or detecting crop related rows, ascrops tend to be closer to a camera system installed above the groundand looking down at an angle than furrows. In an example, the disparitymap can be used to extend the color space of the image, such as bytreating the disparity map as a color dimension, and the adaptive colortransformation techniques described herein can be used to reduce thecolor complexity of the image in the extended color space, such as byprojecting the color and disparity information associated of a pixel onto a single pixel value. Accordingly, any reference to the colors, orthe distribution of colors, of the image in the discussion that follows,can refer to the color information in the original color space or thecolor information and the disparity information in the extended colorspace.

FIG. 1 is an example of an environment including a system 105 foradaptive color transformation to aid CV, according to an embodiment. Theenvironment includes an AEQ 125, such as a tractor, truck, drone, orother vehicle situated proximate to crop related rows. The AEQ 125includes a sensor 120, such as a camera, and the system 105. The sensor120 operates by receiving light, which can be in the visible spectrum,ultraviolet, or infrared, or combinations thereof. In an example, thesensor 120 is only a light sensor, and is not a pattern-based ortiming-based depth sensor, for example. Accordingly, an output of thesensor 120 is an image where elements (e.g., pixels, regions, etc.)represent wavelengths (e.g., colors) or intensities (e.g., luminance) oflight. The sensor 120 can be a stereo vision system having two or morecameras or other sensors for capturing disparity information, such asfor pixels in an image generated by the sensor. An output of the sensor120 can include a disparity map.

In an example, the sensor 120 is mounted to the AEQ 125 and calibratedfor use. Calibration can include such things as providing a mountingheight, perspective angle, position on the AEQ 125, etc. The calibrationparameters can be used in a homography to translate measurements on theimage to the field, for example. These can be used to translate TKE orXTK processed from the image to steering parameters for a steeringcontroller of the AEQ 125. FIGS. 2A and 2B illustrate some additionalaspects of the environment.

The system 105 can include processing circuitry 110 and a memory 115(e.g., computer readable media). The memory 115 is arranged to hold dataand instructions for the processing circuitry 110. To support CVnavigation, including an adaptive color transformation, for the AEQ 125,the processing circuitry 110 is arranged to obtain a color image (e.g.,of a field that includes both crop rows and furrows). In an example thecolor image includes disparity information, such as a color spacedimension (or color channel) representing disparity as an intensityvalue, such that image features that are closer to the sensor 120 havehigher intensity than features that are further away from the sensor.Here, obtaining the image can include the processing circuitry 110retrieving the image from a buffer (e.g., in the memory 115) orreceiving the image (e.g., directly from the sensor 120 via directmemory access (DMA) or the like). (e.g., retrieved or received).

In an example, the color image is preprocessed from a first colorrepresentation that does not include a luminance component of the colorto a second color representation that includes a luminance component.The preprocessing can be accomplished by the processing circuitry 110,circuitry in the sensor 120, or other component of the system 105 or theAEQ 125. In an example, the first color representation is red-green-blue(RGB). Generally, in an RGB image, the intensity of each color for eachpixel is represented in different channels. In an example, the secondcolor representation is one of YUV, YIQ, YDbDr, YCbCr, YPbPr, ICtCp, orxvYCC. In these color representations, like RGB, three values are usedto represent a given color, but the meaning of these values differs.Generally, each has a luma or luminance channel and two chrominancechannels (e.g., hue and saturation, blue—luma and red—luma, etc.). Thus,in these examples, if the original image is not represented by a colorspace containing luminance as one of its explicit components then it isfirst transformed to a color space that does differentiate between colorcontent (e.g., chrominance) and luminance. like YUV, YPbPr, YCbCr orsimilar. Several conversion techniques can be used to accomplish thistransformation, such as the International Telecommunications Union (ITU)Radiocommunication Sector (ITU-R) standards BT.601, BT.709, BT.2020among others. The preprocessing can also include associating thetransformed color image with a corresponding disparity map, such as byadding the disparity information to an additional color channel of thecolor image.

After the color image is obtained and possibly pre-processed, thetechnique involves finding a transformation of the remaining (e.g.,two-channel or three-channel) color to a reduced channel (e.g., a singlechannel) color scale. For example, the transformation will project atwo-dimensional or three-dimensional plane onto a one-dimensional line.To accomplish this, a set of reference points is created from the image.The reference points can be all pixels in the image, a region ofinterest, a uniformly distributed sub-set (e.g., a statisticallyrelevant sample) or any other relevant set of points.

The processing circuitry 110 is arranged to map colors of the image intoa multi-dimensional space to create a distribution of colors in theimage. Here, a dimension of the multi-dimensional space corresponds to acomponent of a color (e.g., hue or saturation). The mapped colors aretaken from the reference points (e.g., pixels) mentioned above. In anexample, where the color representation of the image includes aluminance component (e.g., channel), the luminance component is notmapped to the multi-dimensional space. In an example, themulti-dimensional space is two dimensions (e.g., hue and saturation).Here, luminance is ignored because it generally does not differentiatebetween crop row pixels and furrow pixels and can even bias the results.Thus, eliminating luminance can enable a quick reduction in colorcomplexity with little negative, and perhaps improved, impact on theresult. However, as noted below, if chrominance information alone isinsufficient to differentiate between crop pixels and furrow pixels,luminance or disparity can be mapped into a dimension of themulti-dimensional space to produce a better color transformation.

In an example, mapping the colors of the image to a multi-dimensionalspace includes mapping the color channel that includes the disparityinformation to the multi-dimensional space. In such example, themulti-dimensional space has three dimensions (e.g., hue, saturation, anddisparity). Prior to the mapping, the disparity information can beconditioned, such as by normalizing and bounding the disparityinformation to color information or ranges of the image.

Once mapped into the multi-dimensional space, the processing circuitry110 analyzes the distribution of colors to find out if the distributionhas a direction (e.g., is it stretched in the coordinate system). If thedistribution is stretched, them the processing circuitry 110 ascertainsthe direction of the stretch and uses this as the basis for a projectionfrom one order to another (e.g., from two-dimensions or three-dimensionsto one dimension). If there is no stretch, then the color or disparitydifferentiation between the crop pixels and furrow pixels is likely tooclose for reliable row detection.

To determine the direction of the distribution's stretch, the processingcircuitry 110 is arranged to fit a line to the distribution. Here, theline includes an angle relative to a coordinate system of themulti-dimensional space that represents the direction of the stretch.This approach enables a good (e.g., maximum) spread of the data pointsin the lower dimension after the transformation.

Any line fitting technique can be used. In an example, the line fittingis accomplished by applying a linear regression to the distribution. Anexample is provided below with respect to FIG. 4. In an example, theline fitting is accomplished by clustering points of the distributionand connecting a first centroid of a first cluster to a second centroidof a second cluster to find the line. An example is provided below withrespect to FIG. 5. In an example, the line fitting is accomplished byperforming an angular scan from a centroid of the distribution. Anexample is provided below with respect to FIG. 6. In an example, theline fitting is accomplished by applying a theta distribution. Here, thecoordinate system of the multi-dimensional space is a polar coordinatesystem. An example is provided below with respect to FIG. 7.

Another example line fitting techniques can include applying a randomconsensus sample (RANSAC) line model to the distribution. RANSACoperates similarly to a linear regression with an attempt to rejectoutliers. Generally, a series of linear models are fit to random samplesof the data. The linear model that best fit the data is then selected.This operates on the assumption that inliers have a non-randomrelationship, and thus will exert an influence across the randomsamples. In an example, the line fitting is accomplished by applying astandard deviational ellipse to the distribution. The standarddeviational ellipse is applied by calculating the standard distanceseparately in the dimensions (e.g., x and y directions in atwo-dimensional space). These two measures define the axes of an ellipseencompassing the distribution of features. The major axis of thestandard deviational ellipse can be used at the fit line for thepurposes of finding the angle to the coordinate system.

Other example line fitting techniques include the use of principalcomponent analysis (PCA). Generally, the distribution can be organizedin a data matrix or a data covariance matrix and normalized, such as byzeroing column means or normalizing column variances. The data matrix orcovariance matrix can then be analyzed using singular valuedecomposition or eigenvalue decomposition, respectively, to determinethe principal components of the distribution. For a two-dimensionaldataset, a single calculated principle component can be used to find theangle to the coordinate system. For higher dimensional distributions,regression can be run against two or more principal components todetermine a best fit line.

Once the angle of the line fit to the distribution is found, theprocessing circuitry 110 is arranged to transform the colors in theimage based on the angle to produce a reduced image. Here, thetransformation reduces a color complexity of the image. Generally, thetransformation translates the distance between the distribution pointsof two or three dimensions on to a single dimension, maximize contrast(e.g., separation, distinctiveness, etc.) between the colors. An exampleof the transformation from a two-dimensional space to a one-dimensionalspace can be implemented as follows:

Given:

Direction a vector V=(cos α, sin α); and

Data Points DP=(x, y),

then

Scalar (e.g., one-dimensional) point z=DP×V.

Here, a scale factor can be applied to z to fit preferred data rangesfor crop related row processing.

As noted above, the effectiveness of the transform can be tied to thedirectionality, or stretch, of the color distribution in themulti-dimensional space. Thus, ascertaining the quality of the stretchcan provide information to the system 105 about the likely effectivenessof trying to determine crop related rows from the reduced image,including taking remedial actions. Thus, in an example, the processingcircuitry 110 is arranged to calculate a quality indicator for thetransformation. The quality indicator is useful to know if the resultingangle of the fit line is a useful basis for the transformation. The moredirectional (e.g., stretched or elliptical) the distribution, the betterthe data spread in the reduced dimensional and the better the quality ofthe reduced image for row detection. The more circular the distribution(e.g., all directions approaching equality from a centroid of thedistribution), the lower the quality of the resulting reduced image.

Generally, the quality indicator computation is based in the techniqueuse to find the distribution direction. For example, when using polarcoordinates (r, θ), the standard deviation σ0 of θ is a good qualityindicator. Here, a low σ is more circular (e.g., lower quality) and ahigher σ is more directional (e.g., a higher quality. When RANSAC isemployed to fit the line to the distribution, lines can be randomlytested under different angles to count the number of data points withina certain distance from the line. Here, the quality indicator is higherwhen tested angles correspond to the first-found angle have highercounts. Otherwise, if all counts for all angles are roughly equal, thenthe distribution is more circular, resulting in a lower qualityindicator. Thus, in an example, a calculation of the quality indicatoris based on a line fitting technique employed when fitting the line tothe distribution. In an example, the quality indicator is lower thecloser values become of a major axis and a minor axis of an ellipserepresenting the distribution (e.g., the more circular, rather thanelliptical, the distribution is).

In an example, the mapping of colors of the image into themulti-dimensional space is changed in response to the quality indicatorbeing below a threshold. This is an example of a remedial action whenthe quality indicator is low. In an example, changing the mapping ofcolors includes using a luminance component of the colors when theluminance component was not already used. As noted above, adding theadditional luminance information can, in some circumstances, result insufficient data to differentiate between crop row and furrow pixels.Other examples of remedial actions can include discarding the image andwaiting for a second image, emitting a differentiator, such as aspotlight in white or a particular color (e.g., include infrared orultraviolet), or slowing the AEQ 125 to wait for a better image.

The adaptive color transformation described herein enables robust croprelated row detection considering local disturbances (e.g., the presenceof weeds, missing crops, etc.). Its adaptive nature provides croprelated row detection across a wide range of crop and substrate colors,enabling the technique to be applied, without modification, to differentcrops in different environments.

The processing circuitry 110 is arranged to communicate the reducedimage to a receiver. Generally, a steering controller of the AEQ 125 isthe receiver, however, an intermediary can also be the receiver wherethe intermediary uses the reduced image to calculate the TKE and XTKvalues for the steering controller. In an example, the processingcircuitry 110 is itself the receiver to also process some or all of theTKE or XTK from the reduced image. Communicating the reduced image caninclude placing the reduced image in a data structure in the memory 115or transmitting a representation of the reduced image over an interlink(e.g., bus, network, etc.). In an example, the quality indicator iscommunicated with the reduced image.

FIGS. 2A-2B illustrate various components and relationships of an AEQ inan operating environment, according to an embodiment. FIG. 2A is atop-down view of an AEQ 210 in a field. The shaded portions representcrop rows 215 and the space between the crop rows 215 are furrows 220.The AEQ 210 includes a sensor 205 mounted to the front side of the AEQ210. Here, the AEQ 210 is not aligned with the crop rows 215 or furrows220 but deviates by TKE 225. A steering controller of the AEQ 210 isarranged to steer the AEQ 210 to be in line with the crop rows 215 withwheels in the furrows 220.

FIG. 2B illustrated a side view of the AEQ 210 with the front mountedsensor 205. The height 235 of the sensor 205 and the angle to the ground230 are calibration parameters for the CV system. These parameters canbe provided by a user or can be auto-calibrated. Auto-calibration cantake several forms, such as using a rang-finder (e.g., ultrasonic,radar, laser, etc.), or can use the periodic function fitting describedherein. In the latter case, given a crop type, for example, differentperiods can be tested at a given segment to ascertain which periodresults in a maximum value. The given period is then used as an index tothe height 235 or angle 230 calibration parameter.

FIG. 3 illustrates mapping colors to a multi-dimensional space,according to an embodiment. As illustrated, the colors are mapped to athree-dimensional coordinate system, such as red, green, and blue, orluminance, hue and saturation. In fact, a greater number of dimensionscan be used for images that have greater color depths, such as infraredand ultraviolet, or disparity information. As long as the distributionis not an n-sphere (e.g., a circle when n=2, a sphere when n=3, etc.),then the resulting measurement of the stretch (e.g., line fitting)results in an angle between two of the dimensions that can be used toadaptively reduced the color complexity into a single dimension. In anexample, this process can be repeated until the final reduced image ishas single dimension color complexity. The following line fittingexamples are illustrated in a two-dimensional space, but the techniquesare generally applicable in higher order dimensions.

FIG. 4 illustrates fitting a line to a color distribution using linearregression, according to an embodiment. Here, the line 405 is fit to thedistribution using any one of several linear regression techniques.Examples of these techniques can include a least-squares estimation—suchas ordinary least squares, generalized least squares, generalized leastsquares, percentage least squares, etc.—a maximum-likelihood estimation,Bayesian linear regression, among others.

Depending on which linear regression technique is used, outliers canhave an undesirable influence on the result. Note that the line 405 hasa reduced angle with respect to the illustrated horizontal access thanline 515 or 610 respectively illustrated in FIGS. 5 and 6. Techniques,such as RANSAC, can be used to reduce this effect.

FIG. 5 illustrates fitting a line to a color distribution usingclustering, according to an embodiment. Here, the colors are segmentedinto two clusters (e.g., k-means clustering where k=2). The two clustersare used because of the expectation that two colors will dominate theimage, the crop color and the furrow color. Once the colors areclustered, a centroid is computed for each cluster. Thus, the centroid505 is calculated for the shaded cluster and centroid 510 is calculatedfor the unshaded cluster. The line 515 is fit by connecting the centroid505 to the centroid 510.

FIG. 6 illustrates fitting a line to a color distribution using anangular scan, according to an embodiment. The angular scan operates bycounting points on a line at a given angle through a centroid 610 of thedistribution. The line with the most points is the fit line 605. Forexample, the centroid 610 is calculated. The mean distance of the datapoints in the distribution to the centroid 610 are calculated. This isused to inlier points to test lines in the next operation and can helpto exclude outliers. Next, a series of lines that pass through thecentroid 610 are tested. Each of these test lines is at a differentangle relative to the horizontal axis, for example.

For each test line, count the number of points for which theperpendicular distance to the test line is lower than a pre-definedthreshold (e.g., the previously calculated mean of points to thecentroid 610). These number of inliers are the score for a given line.Once the tests are complete, the line 605 (e.g., angle) with the highestscore is the basis for the transformation.

In this line fitting technique, example quality indicator calculationscan include the standard deviation of the different test line scores ora peak-to-peak difference of the test line scores, such asmax(score)−min (score).

FIG. 7 illustrates fitting a line to a color distribution using a thetadistribution, according to an embodiment. Here, the points in thedistribution are represented as an angle θ and a radius r (e.g., (r, θ))in two dimensions and additional angles in higher dimensions (e.g., (r,θ, ϕ) in three dimensions). The distribution can be centered in thecoordinate system and the angles (e.g., θ from each point) collected todetermine a distribution of the angles. The densest accumulation ofangles is the angle desired for the transformation. Thus, the mean ofangles, median, or other statistical technique can be used to determinethe angle used for the adaptive color transformation.

FIG. 8 illustrates a flow diagram of an example of a method 800 foradaptive color transformation to aid CV, according to an embodiment. Theoperations of the method 800 are implemented and performed by hardware,such as that described above or below (e.g., processing circuitry).

At operation 805, a color image is obtained (e.g., received orretrieved). In an example, the color image is preprocessed from a firstcolor representation that does not include a luminance component of thecolor to a second color representation that includes a luminancecomponent. In an example, the first color representation isred-green-blue (RGB). In an example, the second color representation isone of YUV, YIQ, YDbDr, YCbCr, YPbPr, ICtCp, or xvYCC.

At operation 810, colors of the image are mapped into amulti-dimensional space to create a distribution of colors in the image.Here, a dimension of the multi-dimensional space corresponds to acomponent of a color. In an example, where the color representation ofthe image includes a luminance component (e.g., channel), the luminancecomponent is not mapped to the multi-dimensional space. In an example,the multi-dimensional space is two dimensions (e.g., hue andsaturation).

At operation 815, a line is fit to the distribution. Here, the lineincludes an angle relative to a coordinate system of themulti-dimensional space. In an example, the line fitting is accomplishedby performing an angular scan from a centroid of the distribution. In anexample, the line fitting is accomplished by applying a linearregression to the distribution. In an example, the line fitting isaccomplished by applying a theta distribution. Here, the coordinatesystem of the multi-dimensional space is a polar coordinate system. Inan example, the line fitting is accomplished by applying a randomconsensus sample (RANSAC) line model to the distribution. In an example,the line fitting is accomplished by applying a standard deviationalellipse to the distribution. In an example, the line fitting isaccomplished by clustering points of the distribution and connecting afirst centroid of a first cluster to a second centroid of a secondcluster to find the line.

At operation 820, a transformation is applied to colors in the imagebased on the angle to produce a reduced image. Here, the transformationreduces a color complexity of the image.

At operation 825, the reduced image is communicated to a receiver.

In an example, the method 800 can be extended to include calculating aquality indicator for the transformation. In an example, the qualityindicator is communicated with the reduced image.

In an example, the mapping of colors of the image into themulti-dimensional space is changed in response to the quality indicatorbeing below a threshold. In an example, changing the mapping of colorsincludes using a luminance component of the colors when the luminancecomponent was not already used.

In an example, a calculation of the quality indicator is based on a linefitting technique employed when fitting the line to the distribution. Inan example, the quality indicator is lower the closer values become of amajor axis and a minor axis of an ellipse representing the distribution.

FIG. 9 illustrates a block diagram of an example machine 900 upon whichany one or more of the techniques (e.g., methodologies) discussed hereincan perform. Examples, as described herein, can include, or can operateby, logic or a number of components, or mechanisms in the machine 900.Circuitry (e.g., processing circuitry) is a collection of circuitsimplemented in tangible entities of the machine 900 that includehardware (e.g., simple circuits, gates, logic, etc.). Circuitrymembership can be flexible over time. Circuitries include members thatcan, alone or in combination, perform specified operations whenoperating. In an example, hardware of the circuitry can be immutablydesigned to carry out a specific operation (e.g., hardwired). In anexample, the hardware of the circuitry can include variably connectedphysical components (e.g., execution units, transistors, simplecircuits, etc.) including a machine readable medium physically modified(e.g., magnetically, electrically, moveable placement of invariantmassed particles, etc.) to encode instructions of the specificoperation. In connecting the physical components, the underlyingelectrical properties of a hardware constituent are changed, forexample, from an insulator to a conductor or vice versa. Theinstructions enable embedded hardware (e.g., the execution units or aloading mechanism) to create members of the circuitry in hardware viathe variable connections to carry out portions of the specific operationwhen in operation. Accordingly, in an example, the machine readablemedium elements are part of the circuitry or are communicatively coupledto the other components of the circuitry when the device is operating.In an example, any of the physical components can be used in more thanone member of more than one circuitry. For example, under operation,execution units can be used in a first circuit of a first circuitry atone point in time and reused by a second circuit in the first circuitry,or by a third circuit in a second circuitry at a different time.Additional examples of these components with respect to the machine 900follow.

In alternative embodiments, the machine 900 can operate as a standalonedevice or can be connected (e.g., networked) to other machines. In anetworked deployment, the machine 900 can operate in the capacity of aserver machine, a client machine, or both in server-client networkenvironments. In an example, the machine 900 can act as a peer machinein peer-to-peer (P2P) (or other distributed) network environment. Themachine 900 can be a personal computer (PC), a tablet PC, a set-top box(STB), a personal digital assistant (PDA), a mobile telephone, a webappliance, a network router, switch or bridge, or any machine capable ofexecuting instructions (sequential or otherwise) that specify actions tobe taken by that machine. Further, while only a single machine isillustrated, the term “machine” shall also be taken to include anycollection of machines that individually or jointly execute a set (ormultiple sets) of instructions to perform any one or more of themethodologies discussed herein, such as cloud computing, software as aservice (SaaS), other computer cluster configurations.

The machine (e.g., computer system) 900 can include a hardware processor902 (e.g., a central processing unit (CPU), a graphics processing unit(GPU), a hardware processor core, or any combination thereof), a mainmemory 904, a static memory (e.g., memory or storage for firmware,microcode, a basic-input-output (BIOS), unified extensible firmwareinterface (UEFI), etc.) 906, and mass storage 908 (e.g., hard drive,tape drive, flash storage, or other block devices) some or all of whichcan communicate with each other via an interlink (e.g., bus) 930. Themachine 900 can further include a display unit 910, an alphanumericinput device 912 (e.g., a keyboard), and a user interface (UI)navigation device 914 (e.g., a mouse). In an example, the display unit910, input device 912 and UI navigation device 914 can be a touch screendisplay. The machine 900 can additionally include a storage device(e.g., drive unit) 908, a signal generation device 918 (e.g., aspeaker), a network interface device 920, and one or more sensors 916,such as a global positioning system (GPS) sensor, compass,accelerometer, or other sensor. The machine 900 can include an outputcontroller 928, such as a serial (e.g., universal serial bus (USB),parallel, or other wired or wireless (e.g., infrared (IR), near fieldcommunication (NFC), etc.) connection to communicate or control one ormore peripheral devices (e.g., a printer, card reader, etc.).

Registers of the processor 902, the main memory 904, the static memory906, or the mass storage 908 can be, or include, a machine readablemedium 922 on which is stored one or more sets of data structures orinstructions 924 (e.g., software) embodying or utilized by any one ormore of the techniques or functions described herein. The instructions924 can also reside, completely or at least partially, within any ofregisters of the processor 902, the main memory 904, the static memory906, or the mass storage 908 during execution thereof by the machine900. In an example, one or any combination of the hardware processor902, the main memory 904, the static memory 906, or the mass storage 908can constitute the machine readable media 922. While the machinereadable medium 922 is illustrated as a single medium, the term “machinereadable medium” can include a single medium or multiple media (e.g., acentralized or distributed database, and/or associated caches andservers) configured to store the one or more instructions 924.

The term “machine readable medium” can include any medium that iscapable of storing, encoding, or carrying instructions for execution bythe machine 900 and that cause the machine 900 to perform any one ormore of the techniques of the present disclosure, or that is capable ofstoring, encoding or carrying data structures used by or associated withsuch instructions. Non-limiting machine readable medium examples caninclude solid-state memories, optical media, magnetic media, and signals(e.g., radio frequency signals, other photon based signals, soundsignals, etc.). In an example, a non-transitory machine readable mediumcomprises a machine readable medium with a plurality of particles havinginvariant (e.g., rest) mass, and thus are compositions of matter.Accordingly, non-transitory machine-readable media are machine readablemedia that do not include transitory propagating signals. Specificexamples of non-transitory machine readable media can include:non-volatile memory, such as semiconductor memory devices (e.g.,Electrically Programmable Read-Only Memory (EPROM), ElectricallyErasable Programmable Read-Only Memory (EEPROM)) and flash memorydevices; magnetic disks, such as internal hard disks and removabledisks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

The instructions 924 can be further transmitted or received over acommunications network 926 using a transmission medium via the networkinterface device 920 utilizing any one of a number of transfer protocols(e.g., frame relay, internet protocol (IP), transmission controlprotocol (TCP), user datagram protocol (UDP), hypertext transferprotocol (HTTP), etc.). Example communication networks can include alocal area network (LAN), a wide area network (WAN), a packet datanetwork (e.g., the Internet), mobile telephone networks (e.g., cellularnetworks), Plain Old Telephone (POTS) networks, and wireless datanetworks (e.g., Institute of Electrical and Electronics Engineers (IEEE)802.11 family of standards known as Wi-Fi®, IEEE 802.16 family ofstandards known as WiMax®), IEEE 802.15.4 family of standards,peer-to-peer (P2P) networks, among others. In an example, the networkinterface device 920 can include one or more physical jacks (e.g.,Ethernet, coaxial, or phone jacks) or one or more antennas to connect tothe communications network 926. In an example, the network interfacedevice 920 can include a plurality of antennas to wirelessly communicateusing at least one of single-input multiple-output (SIMO),multiple-input multiple-output (MIMO), or multiple-input single-output(MISO) techniques. The term “transmission medium” shall be taken toinclude any intangible medium that is capable of storing, encoding orcarrying instructions for execution by the machine 900, and includesdigital or analog communications signals or other intangible medium tofacilitate communication of such software. A transmission medium is amachine readable medium.

Additional Notes & Examples

Example 1 Example 1 is a device for adaptive color transformation to aidcomputer vision, the device comprising: a memory including instructions;and processing circuitry that is configured by the instructions to:obtain a color image; map colors of the image into a multi-dimensionalspace to create a distribution of colors in the image, a dimension ofthe multi-dimensional space corresponding to a component of a color; fita line to the distribution, the line including an angle relative to acoordinate system of the multi-dimensional space; apply a transformationto colors in the image based on the angle to produce a reduced image,the transformation reducing a color complexity; and communicate thereduced image to a receiver.

In Example 2, the subject matter of Example 1 includes, wherein thecolor image is preprocessed from a first color representation that doesnot include a luminance component of the color to a second colorrepresentation that includes a luminance component.

In Example 3, the subject matter of Example 2 includes, wherein thefirst color representation is red-green-blue.

In Example 4, the subject matter of Examples 2-3 includes, wherein thesecond color representation is one of YUV, YIQ, YDbDr, YCbCr, YPbPr,ICtCp, or xvYCC.

In Example 5, the subject matter of Examples 2-4 includes, wherein theluminance component is not mapped to the multi-dimensional space.

In Example 6, the subject matter of Examples 1-5 includes, wherein themulti-dimensional space is two dimensions.

In Example 7, the subject matter of Examples 1-6 includes, wherein thefirst color representation or the second color representation includes adepth component.

In Example 8, the subject matter of Examples 1-7 includes, wherein, tofit the line to the distribution, the processing circuitry is arrangedby the instructions to apply a linear regression to the distribution tofind the line.

In Example 9, the subject matter of Examples 1-8 includes, wherein, tofit the line to the distribution, the processing circuitry is arrangedby the instructions to performing an angular scan from a centroid of thedistribution to find the line.

In Example 10, the subject matter of Examples 1-9 includes, wherein, tofit the line to the distribution includes, the processing circuitry isarranged by the instructions to apply a theta distribution, wherein acoordinate system of the multi-dimensional space is a polar coordinatesystem.

In Example 11, the subject matter of Examples 1-10 includes, wherein, tofit the line to the distribution, the processing circuitry is arrangedby the instructions to apply a random consensus sample (RANSAC) linemodel to the distribution.

In Example 12, the subject matter of Examples 1-11 includes, wherein, tofit the line to the distribution, the processing circuitry is arrangedby the instructions to apply a standard deviational ellipse to thedistribution.

In Example 13, the subject matter of Examples 1-12 includes, wherein, tofit the line to the distribution, the processing circuitry is arrangedby the instructions to: cluster points of the distribution; and connecta first centroid of a first cluster to a second centroid of a secondcluster to find the line.

In Example 14, the subject matter of Examples 1-13 includes, wherein theinstructions cause the processing circuitry to calculate a qualityindicator for the transformation.

In Example 15, the subject matter of Example 14 includes, wherein, tocommunicate the reduced image, the processing circuitry is arranged bythe instructions to communicate the quality indicator.

In Example 16, the subject matter of Examples 14-15 includes, whereinthe processing circuitry is arranged by the instructions to change themapping of colors of the image into the multi-dimensional space inresponse to the quality indicator being below a threshold.

In Example 17, the subject matter of Example 16 includes, wherein, tochange the mapping of colors, the processing circuitry is arranged bythe instructions to use a luminance component of the colors when theluminance component was not already used.

In Example 18, the subject matter of Examples 14-17 includes, wherein acalculation of the quality indicator is based on a line fittingtechnique employed when fitting the line to the distribution.

In Example 19, the subject matter of Example 18 includes, wherein thequality indicator is lower the closer values become of a major axis anda minor axis of an ellipse representing the distribution.

In Example 20, the subject matter of Examples 18-19 includes, whereinthe quality indicator is lower the closer values become of a major axisand a minor axis of an ellipse representing the distribution.

Example 21 is a method for adaptive color transformation to aid computervision, the method comprising: obtaining a color image; mapping colorsof the image into a multi-dimensional space to create a distribution ofcolors in the image, a dimension of the multi-dimensional spacecorresponding to a component of a color; fitting a line to thedistribution, the line including an angle relative to a coordinatesystem of the multi-dimensional space; applying a transformation tocolors in the image based on the angle to produce a reduced image, thetransformation reducing a color complexity; and communicating thereduced image to a receiver.

In Example 22, the subject matter of Example 21 includes, wherein thecolor image is preprocessed from a first color representation that doesnot include a luminance component of the color to a second colorrepresentation that includes a luminance component.

In Example 23, the subject matter of Example 22 includes, wherein thefirst color representation is red-green-blue.

In Example 24, the subject matter of Examples 22-23 includes, whereinthe second color representation is one of YUV, YIQ, YDbDr, YCbCr, YPbPr,ICtCp, or xvYCC.

In Example 25, the subject matter of Examples 22-24 includes, whereinthe luminance component is not mapped to the multi-dimensional space.

In Example 26, the subject matter of Examples 21-25 includes, whereinthe multi-dimensional space is two dimensions.

In Example 27, the subject matter of Examples 21-26 includes, whereinfitting the line to the distribution includes applying a linearregression to the distribution to find the line.

In Example 28, the subject matter of Examples 21-27 includes, whereinfitting the line to the distribution includes performing an angular scanfrom a centroid of the distribution to find the line.

In Example 29, the subject matter of Examples 21-28 includes, whereinfitting the line to the distribution includes applying a thetadistribution, wherein a coordinate system of the multi-dimensional spaceis a polar coordinate system.

In Example 30, the subject matter of Examples 21-29 includes, whereinfitting the line to the distribution includes applying a randomconsensus sample (RANSAC) line model to the distribution.

In Example 31, the subject matter of Examples 21-30 includes, whereinfitting the line to the distribution includes applying a standarddeviational ellipse to the distribution.

In Example 32, the subject matter of Examples 21-31 includes, whereinfitting the line to the distribution includes: clustering points of thedistribution; and connecting a first centroid of a first cluster to asecond centroid of a second cluster to find the line.

In Example 33, the subject matter of Examples 21-32 includes,calculating a quality indicator for the transformation.

In Example 34, the subject matter of Example 33 includes, whereincommunicating the reduced image includes communicating the qualityindicator.

In Example 35, the subject matter of Examples 33-34 includes, changingthe mapping of colors of the image into the multi-dimensional space inresponse to the quality indicator being below a threshold.

In Example 36, the subject matter of Example 35 includes, whereinchanging the mapping of colors includes using a luminance component ofthe colors when the luminance component was not already used.

In Example 37, the subject matter of Examples 33-36 includes, wherein acalculation of the quality indicator is based on a line fittingtechnique employed when fitting the line to the distribution.

In Example 38, the subject matter of Example 37 includes, wherein thequality indicator is lower the closer values become of a major axis anda minor axis of an ellipse representing the distribution.

Example 39 is a machine readable medium including instructions foradaptive color transformation to aid computer vision, the instructions,when executed by processing circuitry, cause the processing circuitry toperform operations comprising: obtaining a color image; mapping colorsof the image into a multi-dimensional space to create a distribution ofcolors in the image, a dimension of the multi-dimensional spacecorresponding to a component of a color; fitting a line to thedistribution, the line including an angle relative to a coordinatesystem of the multi-dimensional space; applying a transformation tocolors in the image based on the angle to produce a reduced image, thetransformation reducing a color complexity; and communicating thereduced image to a receiver.

In Example 40, the subject matter of Example 39 includes, wherein thecolor image is preprocessed from a first color representation that doesnot include a luminance component of the color to a second colorrepresentation that includes a luminance component.

In Example 41, the subject matter of Example 40 includes, wherein thefirst color representation is red-green-blue.

In Example 42, the subject matter of Examples 40-41 includes, whereinthe second color representation is one of YUV, YIQ, YDbDr, YCbCr, YPbPr,ICtCp, or xvYCC.

In Example 43, the subject matter of Examples 40-42 includes, whereinthe luminance component is not mapped to the multi-dimensional space.

In Example 44, the subject matter of Examples 39-43 includes, whereinthe multi-dimensional space is two dimensions.

In Example 45, the subject matter of Examples 39-44 includes, whereinfitting the line to the distribution includes applying a linearregression to the distribution to find the line.

In Example 46, the subject matter of Examples 39-45 includes, whereinfitting the line to the distribution includes performing an angular scanfrom a centroid of the distribution to find the line.

In Example 47, the subject matter of Examples 39-46 includes, whereinfitting the line to the distribution includes applying a thetadistribution, wherein a coordinate system of the multi-dimensional spaceis a polar coordinate system.

In Example 48, the subject matter of Examples 39-47 includes, whereinfitting the line to the distribution includes applying a randomconsensus sample (RANSAC) line model to the distribution.

In Example 49, the subject matter of Examples 39-48 includes, whereinfitting the line to the distribution includes applying a standarddeviational ellipse to the distribution.

In Example 50, the subject matter of Examples 39-49 includes, whereinfitting the line to the distribution includes: clustering points of thedistribution; and connecting a first centroid of a first cluster to asecond centroid of a second cluster to find the line.

In Example 51, the subject matter of Examples 39-50 includes, whereinthe operations comprise calculating a quality indicator for thetransformation.

In Example 52, the subject matter of Example 51 includes, whereincommunicating the reduced image includes communicating the qualityindicator.

In Example 53, the subject matter of Examples 51-52 includes, whereinthe operations comprise changing the mapping of colors of the image intothe multi-dimensional space in response to the quality indicator beingbelow a threshold.

In Example 54, the subject matter of Example 53 includes, whereinchanging the mapping of colors includes using a luminance component ofthe colors when the luminance component was not already used.

In Example 55, the subject matter of Examples 51-54 includes, wherein acalculation of the quality indicator is based on a line fittingtechnique employed when fitting the line to the distribution.

In Example 56, the subject matter of Example 55 includes, wherein thequality indicator is lower the closer values become of a major axis anda minor axis of an ellipse representing the distribution.

Example 57 is a system for adaptive color transformation to aid computervision, the system comprising: means for obtaining a color image; meansfor mapping colors of the image into a multi-dimensional space to createa distribution of colors in the image, a dimension of themulti-dimensional space corresponding to a component of a color; meansfor fitting a line to the distribution, the line including an anglerelative to a coordinate system of the multi-dimensional space; meansfor applying a transformation to colors in the image based on the angleto produce a reduced image, the transformation reducing a colorcomplexity; and means for communicating the reduced image to a receiver.

In Example 58, the subject matter of Example 57 includes, wherein thecolor image is preprocessed from a first color representation that doesnot include a luminance component of the color to a second colorrepresentation that includes a luminance component.

In Example 59, the subject matter of Example 58 includes, wherein thefirst color representation is red-green-blue.

In Example 60, the subject matter of Examples 58-59 includes, whereinthe second color representation is one of YUV, YIQ, YDbDr, YCbCr, YPbPr,ICtCp, or xvYCC.

In Example 61, the subject matter of Examples 58-60 includes, whereinthe luminance component is not mapped to the multi-dimensional space.

In Example 62, the subject matter of Examples 57-61 includes, whereinthe multi-dimensional space is two dimensions.

In Example 63, the subject matter of Examples 57-62 includes, whereinthe means for fitting the line to the distribution include means forapplying a linear regression to the distribution to find the line.

In Example 64, the subject matter of Examples 57-63 includes, whereinthe means for fitting the line to the distribution include means forperforming an angular scan from a centroid of the distribution to findthe line.

In Example 65, the subject matter of Examples 57-64 includes, whereinthe means for fitting the line to the distribution include means forapplying a theta distribution, wherein a coordinate system of themulti-dimensional space is a polar coordinate system.

In Example 66, the subject matter of Examples 57-65 includes, whereinthe means for fitting the line to the distribution include means forapplying a random consensus sample (RANSAC) line model to thedistribution.

In Example 67, the subject matter of Examples 57-66 includes, whereinthe means for fitting the line to the distribution include means forapplying a standard deviational ellipse to the distribution.

In Example 68, the subject matter of Examples 57-67 includes, whereinthe means for fitting the line to the distribution include: means forclustering points of the distribution; and means for connecting a firstcentroid of a first cluster to a second centroid of a second cluster tofind the line.

In Example 69, the subject matter of Examples 57-68 includes, means forcalculating a quality indicator for the transformation.

In Example 70, the subject matter of Example 69 includes, wherein themeans for communicating the reduced image include means forcommunicating the quality indicator.

In Example 71, the subject matter of Examples 69-70 includes, means forchanging the mapping of colors of the image into the multi-dimensionalspace in response to the quality indicator being below a threshold.

In Example 72, the subject matter of Example 71 includes, wherein themeans for changing the mapping of colors include means for using aluminance component of the colors when the luminance component was notalready used.

In Example 73, the subject matter of Examples 69-72 includes, wherein acalculation of the quality indicator is based on a line fittingtechnique employed when fitting the line to the distribution.

In Example 74, the subject matter of Example 73 includes, wherein thequality indicator is lower the closer values become of a major axis anda minor axis of an ellipse representing the distribution.

Example 75 is at least one machine-readable medium includinginstructions that, when executed by processing circuitry, cause theprocessing circuitry to perform operations to implement of any ofExamples 1-74.

Example 76 is an apparatus comprising means to implement of any ofExamples 1-74.

Example 77 is a system to implement of any of Examples 1-74.

Example 78 is a method to implement of any of Examples 1-74.

The above detailed description includes references to the accompanyingdrawings, which form a part of the detailed description. The drawingsshow, by way of illustration, specific embodiments that can bepracticed. These embodiments are also referred to herein as “examples.”Such examples can include elements in addition to those shown ordescribed. However, the present inventors also contemplate examples inwhich only those elements shown or described are provided. Moreover, thepresent inventors also contemplate examples using any combination orpermutation of those elements shown or described (or one or more aspectsthereof), either with respect to a particular example (or one or moreaspects thereof), or with respect to other examples (or one or moreaspects thereof) shown or described herein.

All publications, patents, and patent documents referred to in thisdocument are incorporated by reference herein in their entirety, asthough individually incorporated by reference. In the event ofinconsistent usages between this document and those documents soincorporated by reference, the usage in the incorporated reference(s)should be considered supplementary to that of this document; forirreconcilable inconsistencies, the usage in this document controls.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In the appended claims, the terms “including” and“in which” are used as the plain-English equivalents of the respectiveterms “comprising” and “wherein.” Also, in the following claims, theterms “including” and “comprising” are open-ended, that is, a system,device, article, or process that includes elements in addition to thoselisted after such a term in a claim are still deemed to fall within thescope of that claim. Moreover, in the following claims, the terms“first,” “second,” and “third,” etc. are used merely as labels, and arenot intended to impose numerical requirements on their objects.

The above description is intended to be illustrative, and notrestrictive. For example, the above-described examples (or one or moreaspects thereof) can be used in combination with each other. Otherembodiments can be used, such as by one of ordinary skill in the artupon reviewing the above description. The Abstract is to allow thereader to quickly ascertain the nature of the technical disclosure andis submitted with the understanding that it will not be used tointerpret or limit the scope or meaning of the claims. Also, in theabove Detailed Description, various features can be grouped together tostreamline the disclosure. This should not be interpreted as intendingthat an unclaimed disclosed feature is essential to any claim. Rather,inventive subject matter can lie in less than all features of aparticular disclosed embodiment. Thus, the following claims are herebyincorporated into the Detailed Description, with each claim standing onits own as a separate embodiment. The scope of the embodiments should bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

1. (canceled)
 2. A system for adaptive color transformation to aidcomputer vision, the system comprising: a first sensor configured forcoupling with an agricultural vehicle, the first sensor configured togenerate a color image of at least one row of an agricultural fieldhaving one or more crop rows and intervening furrows; a second sensorconfigured for coupling with the agricultural vehicle, the second sensorconfigured to generate a disparity image corresponding to the colorimage, the disparity image including distance data indicative ofdistances between the second sensor and the agricultural field; aprocessor in communication with the first and second sensors, theprocessor includes: a mapping module configured to map colors of thecolor image and the distance data of the disparity image to adistribution in a multi-dimensional space, the multi-dimensional spacehaving a first dimension corresponding to one or more constituentcomponents of the colors and a second dimension corresponding to thedistance data; a line fitting module to fit a line to the distributionin the multi-dimensional space; a filter module to generate a reducedimage having a combined image dimension, the combined image dimensionobtained by using an angle between the line and the first dimensions orthe second dimension to map the one or more constituent components ofthe colors or the distance data to the combined image dimension; and anavigation controller in communication with the processor, thenavigation controller configured to receive the reduced image andconduct guidance of the agricultural vehicle based on the reduced imageand combined image dimension.
 3. The system of claim 2, wherein thefirst and second sensors are a same sensor configured to detect colorand distance information.
 4. The system of claim 2, wherein each pixelin the disparity image is indicative of a distance between the secondsensor and a corresponding region in the agricultural field.
 5. Thesystem of claim 2, wherein using angle between the line and the firstdimensions or the second dimension to map the one or more constituentcomponents of the colors and the distance data to the combined imagedimension increases a contrast between the one or more constituentcomponents of the colors or the distance data in the reduced image. 6.The system of claim 5, wherein the navigation controller is configuredto discern a crop row from a furrow based on the increased contrastbetween the one or more constituent components of the colors or thedistance data in the reduced image.
 7. The system of claim 2, wherein,to fit the line to the distribution, the line fitting module includes amodule to apply a linear regression to the distribution to find theline.
 8. The System of claim 2, wherein, to fit the line to thedistribution, the line fitting module includes a module to perform anangular scan from a centroid of the distribution to find the line. 9.The system of claim 2, wherein, to fit the line to the distribution, theline fitting module includes a module to apply a theta distribution,wherein a coordinate system of the multi-dimensional space is a polarcoordinate system.
 10. The system of claim 2, wherein, to fit the lineto the distribution, the line fitting module includes a module to applya random consensus sample (RANSAC) line model to the distribution. 11.The system of claim 2, wherein, to fit the line to the distribution, theline fitting module includes a module to apply a standard deviationalellipse to the distribution.
 12. The system of claim 2, wherein, to fitthe line to the distribution, the line fitting module includes a moduleto: cluster points of the distribution; and connect a first centroid ofa first cluster to a second centroid of a second cluster to find theline.
 13. The system of claim 2, wherein the color image is preprocessedfrom a first color representation that does not include a luminancecomponent of the colors of the color image to a second colorrepresentation that includes a luminance component.
 14. A system foradaptive image transformation to aid computer vision, the systemcomprising: one or more sensors configured for coupling with anagricultural vehicle, the one or more sensors configured to generate animage of at least one row of an agricultural field, the image havingpixels, each of the pixels includes a positional element and aninformational element, the agricultural field having one or more croprows and intervening furrows; a processor in communication with the oneor more sensors, the processor includes: a mapping module configured tomap the informational elements of the pixels of the image to adistribution in a multi-dimensional space, the multi-dimensional spacehaving a first dimension corresponding to a first constituent componentof the informational element of the pixels and a second dimensioncorresponding to a second constituent component of the informationalelement of the pixels; a line fitting module to fit a line to thedistribution in the multi-dimensional space; a filter module to generatea reduced image having a combined image dimension, the combined imagedimension obtained by using an angle between the line and the first orsecond dimensions to map the first and second constituent components ofthe informational elements of pixels to the combined image dimension;and a navigation controller in communication with the processor, thenavigation controller configured to receive the reduced image andconduct guidance of the agricultural vehicle based on the reduced imageand combined image dimension.
 15. The system of claim 14, wherein usingangle between the line and the first dimensions or the second dimensionto map the first and second constituent components of the informationalelement of pixels to the combined image dimension increases a. contrastbetween the informational element of pixels in the reduced image. 16.The system of claim 14, wherein the one or more sensors include a colorimage sensor, and the first and second constituent components of theinformational element of the pixels are constituent components of colorsof the pixels.
 17. The system of claim 14, wherein the one or moresensors include a depth sensor, and the first and second constituentcomponents of the informational element of the pixels are distances tothe agricultural field.
 18. The system of claim 15, wherein: the one ormore sensors include a color image sensor and a disparity sensor; thefirst constituent component of the informational element of the pixelsinclude a constituent component of colors of the pixels; and the secondconstituent component of the informational element of the pixels includedistances to the agricultural field.
 19. The system of claim 18, whereinthe color image sensor and the disparity sensor are a same sensorconfigured to detect color and distance.
 20. The system of claim 15,wherein the navigation controller is configured to discern a crop rowfrom a furrow based on the increased contrast between the informationalelement of pixels in the reduced image.
 21. A method for adaptive colortransformation to aid computer vision, the method comprising: obtaininga color image of at least one row of an agricultural field having one ormore crop rows or intervening furrows; obtaining a disparity imagecorresponding to the color image, the disparity image including distancedata indicative of distances between a sensor that captured thedisparity image and the one or more crop rows or intervening furrows;mapping colors of the color image and the distance data of the disparityimage to a distribution in a multi-dimensional space, themulti-dimensional space having a first dimension corresponding to one ormore constituent components of the colors and a second dimensioncorresponding to the distance data; fitting a line to the distributionin the multi-dimensional space; generating a reduced image having acombined image dimension, the combined image dimension obtained by usingan angle between the line and the first dimension or the seconddimension to map the one or more constituent components of the colors orthe distance data to the combined image dimension; and communicating thereduced image to a navigation controller.
 22. The method of claim 21,wherein using the angle between the line and the first dimensions or thesecond dimension to map the one or more constituent components of thecolors and the distance data to the combined image dimension increases acontrast between the one or more constituent components of the colors orthe distance data in the reduced image.